(1) Field of the Invention
The present invention relates to a method for providing an estimate of the position, speed, and direction of travel of a contact or target and a system for performing said method.
(2) Description of the Prior Art
A variety of different devices and methods have been used in the prior art to estimate various physical states using sensor information. U.S. Pat. No. 4,965,732 to Roy III et al., for example, illustrates a method and apparatus for signal reception and parameter estimation which may be used for frequency estimation and filtering, array data processing and the like. The Roy III et al. invention is applicable in the context of array data processing to a number of areas including cellular mobile communications, space antennas, sonobuoys, towed arrays of acoustic sensors, and structural analysis.
The method set forth in the Roy III patent comprises the steps of: (a) providing an array of at least one group of a plurality of signal sensor pairs, the sensors in each pair being identical and the displacement between sensors of each pair in a group being equal, thereby defining two subarrays; (b) obtaining signal measurements with the signal array so configured; (c) processing the signal measurements from the towed subarrays to identify the number of sources and estimate parameters thereof, including identifying eigenvalues from which source number and parameter estimates are based; (d) solving the signal copy problem and determining array response (direction) vector using the generalized eigenvectors; and (e) estimating the array geometry from the array response vectors.
The Roy III apparatus includes an array of at least one group of a plurality of signal sensor pairs for generating signals, the sensors in each pair being identical and the displacement between sensors of each pair in a group being equal, thereby defining two subarrays, and signal processing means for processing the signals from the two subarrays to identify the number of sources and estimate parameters thereof.
Another area where this type of technology has been employed is underwater tracking systems. U.S. Pat. Nos. 5,033,034 to Paradise and 5,036,498 to Van Cappel illustrate two such systems.
The Paradise patent relates to an apparatus located aboard a platform situated in an acoustic environment, such as a submarine, for tracking a moving body, such as a torpedo, when the body is proximate to the platform. The apparatus includes a number of acoustic sensor elements at selected locations around the platform. Each of the sensor elements detects acoustic information arriving at its selected location. The apparatus further includes signal conditioners coupled to the sensor elements with a given one of the signal conditioners providing a conditioned signal representing acoustic information which is emitted by the moving body when the body is proximate to the platform, and which arrives at the location of the acoustic sensor element to which the given signal conditioner is coupled. A processor receives conditioned signals from respective signal conditioners and enables comparisons of selected characteristics of the received signals to the made in order to determine a selected parameter which is related to the movement of the body when the moving body is proximate to the platform.
The Van Cappel patent relates to a method for determining the motion of a target in underwater acoustics by means of an antenna with misaligned sensors provided with a central sensor. The method estimates the characteristics of the velocity and position of the target relative to the antenna by means of a likelihood maximum estimator which takes into account the differences in propagation times measured between the wave fronts transmitted by the target and reaching the sensors. The motion estimator is initialized by means of an initial state vector determined on the basis of the values of the azimuths of the target perceived from the mid-points of each pair of sensors, during a determined number of measurements staggered in time. The action of the state vector takes place in taking account of the value of the elevation of the target with respect to the antenna.
In recent years, computer based technology has advanced to the point where artificial systems have been developed which simulate the operation of the human brain. These systems are known as neural networks. Typically, the systems use numerous nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural networks. Each computational element or neuron is connected via weights or synapses that are adapted during training to improve performance. Many of these systems exhibit self-learning by changing their synaptic weights until the correct output is achieved in response to a particular input. As a consequence, these systems have lent themselves to use in a number of different applications.
One such application is target imaging and identification systems. U.S. Pat. No. 4,995,088 to Farhat illustrates a data analysis system for such an application. Farhat's data analysis system comprises a first array for receiving input data comprising a plurality of neural elements for transmitting data signals and memory means for processing the data signals transmitted by the elements of the first array. The memory means has associatively stored therein in accordance with a Hebbian model of learning for neural networks, at least one quantized feature space classifier of a known data set. The system further comprises a second array having a plurality of neural elements for receiving the data signals processed by the memory matrix. The neural elements of the second array are operatively coupled in feedback with the neural elements of the first array wherein the neural elements of the second array provide feedback input for the neural elements of the first array. In a preferred embodiment of the Farhat system, the neural elements of the first array comprise light emitting elements and the neural elements of the second array photo-detectors.
The general contact state estimation, or target motion analysis, problem is to estimate contact location and motion from available sensor readings. These sensors may, or may not be associated with a single observation platform. Applicants have found that in either case a data fusion technique must be employed in order to exploit the available data in ascertaining a contact's state.
In a broad sense, each sensor reading provides constraints on the contact state. For example, a line-of-sight bearing reading of 305 degrees at time 0700 constrains the contact to be somewhere on a line northwest from the observer's location at 0700. If sufficient observations are available, and if assumptions are made about the contact motion (such as constant speed and heading), then the contact state may be constrained to a single solution. In this case, the contact state is said to be observable. A great deal of work has been done in determining the circumstances under which various aspects of contact state are observable; the important point here is that contact state estimation is a constraint problem.
Due to uncertainty, or error, associated with physical sensor readings, contact state determination is indeed a parameter estimation problem. Even though the contact state may be fully observable, noisy sensor readings will preclude an exact solution (truth) for a contact state. A method must be employed to determine the most likely estimate of the contact state. Typically, mathematical estimation techniques such as least squares or maximum likelihood are employed, based on some measure of compliance between actual observations and hypothesis-predicted observations. A related issue is solution sensitivity. Given that the observations are noisy, which non-optimal contact state solutions are above a certain degree of likelihood? Furthermore, are the almost-optimal solutions tightly or loosely clustered in the contact state space?
The major disadvantages associated with current methods are their significant computational demands, difficulties with solution sensitivity assessment, and fusion of multiple sensor information.